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Abstract:

The present invention relates to methods for evaluating biomarkers. In
particular, the invention relates to a method for establishing at least
one pattern for at least one pre-defined effector having at least one
effect on a biological system, which effect is capable of being
determined, to a method of establishing a class of effectors for a
pre-defined effect or group of effects, to a method for identifying at
least one effect of a pre-defined effector and to a computer program and
a computer adapted to carry out these methods.

Claims:

1.-18. (canceled)

19. A method for establishing at least one pattern for at least one
pre-defined effector having at least one determinable effect on a
biological system, comprising the following steps: a) providing at least
one profile of the at least one pre-defined effector; b) comparing at
least one value of at least one biomarker of the at least one profile,
with at least one significance threshold in order to ascertain whether
the at least one biomarker is significant; c) combining significant
biomarkers of the at least one profile to give a pattern.

20. The method according to claim 19, further comprising: d) providing a
database in which profiles are stored for a multiplicity of further
effectors; e) establishing at least one further pattern of at least one
further effector of the database.

21. The method according to claim 20, further comprising: f) comparing
the at least one further pattern established in method step e) with the
pattern established in method step c).

22. The method according to claim 21, further comprising: g) checking
whether the at least one further effector has at least one known effect;
h) comparing the at least one known effect with the previously determined
determinable effect of the pre-defined effector.

23. The method according to claim 22, further comprising: i) if at least
partial match of the patterns is found in method step f), carrying out
the following step: i1) if no match is found in method step h): changing
the significance threshold, in particular increasing the significance
threshold, and repeating at least method steps b) and c).

24. The method according to claim 23, wherein method steps i) and i1) are
carried out repeatedly with a stepwise increase of the significance
threshold.

25. The method according to claim 19, further comprising: j) if no match
of the patterns is found in method step f), carry out the following step:
j1) if a match is found in method step h): changing the significance
threshold, in particular reducing the significance threshold, and
repeating at least method steps b) and c).

26. The method according to claim 25, wherein method steps j) and j1) are
carried out repeatedly with stepwise reduction of the significance
threshold.

27. A method of establishing a class of effectors for a pre-defined
effect or group of effects, comprising the following steps: A) specifying
at least one effector which is presumed to be assigned to the class of
effectors, and assignation to the class of effectors; B) establishing or
updating at least one pattern of the at least one effector, by the method
according to claim 19; C) providing a database in which profiles are
stored for a multiplicity of further effectors; D) searching for
effectors with identical or similar profiles in the database; E)
assigning the effectors determined in step D) to the class of effectors.

28. The method according to claim 27, wherein all or at least one of
steps B) to E) are carried out repeatedly.

29. The method according to claim 19, wherein expert knowledge is
employed in step A).

30. A method for identifying at least one effect of a pre-defined
effector, comprising the following steps: i) establishing at least one
class of effectors for at least one known effect, by the method according
to claim 27; ii) establishing at least one pattern of the pre-defined
effector by: a) providing at least one profile of the at least one
pre-defined effector; b) comparing at least one value of at least one
biomarker of the at least one profile, with at least one significance
threshold in order to ascertain whether the at least one biomarker is
significant; c) combining significant biomarkers of the at least one
profile to give a pattern; iii) comparing the pattern established in step
ii) with the pattern of the class of effectors established in step i).

31. The method according to claim 30, wherein, if a match or a similarity
is found in step iii), the known effect of the class of effectors is
equated with the effect to be ascertained of the pre-defined effector.

32. A method for identifying at least one effect of a pre-defined
effector, comprising the following steps: I) establishing at least one
pattern of the pre-defined effector, by a method according to claim 19;
II) providing a database in which profiles are stored for a multiplicity
of further effectors; III) searching in the database for effectors with a
similar or identical pattern to the pattern established in step I); IV)
checking whether the effectors ascertained in step III) have at least one
known effect; V) if a known effect is found in step IV), equating the
effect of the pre-defined effector with the known effect.

33. A computer program having a program code for implementing the method
according to claim 19 when the program is run in a computer.

34. The computer program according to claim 33, stored on a
machine-readable medium.

35. A computer, adapted to carry out a method according to claim 19.

36. A data medium on which a data structure is stored, which carries out
the method according to claim 19, after loading in a working and/or main
memory of a computer or computer network.

Description:

[0001] The present invention relates to methods for evaluating biomarkers.
In particular, the invention relates to a method for establishing at
least one pattern for at least one pre-defined effector having at least
one effect on a biological system, which effect is capable of being
determined, to a method of establishing a class of effectors for a
pre-defined effect or group of effects, to a method for identifying at
least one effect of a pre-defined effector and to a computer program and
a computer adapted to carry out these methods.

[0002] Biological systems such as individual organisms or populations of
organisms will, as a rule, respond to effectors with a change of state,
in particular a change in their biochemical properties or biochemical
constitution. In this context, an individual organism on which an
external or internal effector acts responds, for example, by modified
cell activities. This modified activity will then also result in a change
in the constitution or quantitative composition of the cellular
molecules. In this context, both changes in the transcriptional
activities or the protein function and protein turnover as well as
metabolic changes may be observed. The latter will, consequently, lead to
a change in the qualitative and/or quantitative metabolite constitution
of the organism as a result of the effector (modification of the
metabolome). Similar changes in the biochemical constitution or
properties can be observed in populations of organisms which form a
biological system. Such populations of organisms which form a biological
system are, for example, microorganisms which form a locally delimitable
micro ecosystem.

[0003] For many biologically relevant questions it is necessary to assess
the influence of effectors on biological systems. In this manner, it is
possible better to avoid or to exploit interfering or advantageous
influences of effectors. For example, chemicals acting as effectors may
have an e.g. toxic effect on a biological system or else a beneficial or
healing effect. Almost all effectors ranging from chemicals through
physical influences such as radiation to intended or unintended genetic
modifications will, ultimately, influence the metabolome. This influence
frequently already occurs at a very early stage after the action of the
effector so that modifications of the metabolome can be used as an early
detection mechanism for particular effects or consequences which an
effector may bring about.

[0004] Modifications of the metabolome, induced by effectors, will, as a
rule, affect not only one metabolite whose status might then be used as
what is known as a biomarker. Frequently, a variety of metabolites are
affected. Effectors which mediate the same effect need not always modify
the same metabolites in this context. However, as a rule, there is a set
of key metabolites which is modified by effectors which mediate the same
effect. This set of modified key metabolites can currently not always be
identified in an efficient manner. Above all, the problem is that most
effectors cause not only the characteristic key metabolites, but also
individual metabolite modifications which are characteristic only of the
individual effector, but are not caused by other effectors which cause
the same effect. In addition, there are metabolic modifications which are
not related to the effector applied, but are induced by other influences
or by variations of the metabolites which are merely caused by the
variability due to measurement techniques.

[0005] Nevertheless, it would be useful for a very wide range of
applications to extract, from a metabolome, the key metabolites which are
modified early as a response of a biological system to specific
effectors. In this manner, chemicals which are toxic to biological
systems might be identified even at an early point in time. Likewise, the
therapeutic activity of candidate active substances might be determined
at an early point in time and in a reliable manner, and potential side
effects might be ruled out. Advantageous or harmful influences of
environmental factors for biological systems in general might likewise be
identified. Ultimately, diseases might also be identified earlier, and
advantageous or disadvantageous effects of modifications of the genetic
material might be studied better.

[0006] It is an object of the present invention to provide a method by
means of which responses of biological systems to specific effectors can
efficiently be studied or predicted, or by means of which a deliberate
search can be made for particular effectors capable of causing a
predetermined response of the biological system.

[0007] This object is achieved by methods and computer programs with the
features of the independent claims; advantageous refinements of the
invention, which may be implemented individually or in any desired
combination, are presented in the dependent claims.

[0008] The methods comprise the method steps described below. The method
steps are preferably carried out in the order presented. In principle,
however, it is also possible to carry out individual or several method
steps in a different order. Thus, for example, it is also possible to
carry out individual or several method steps chronologically in parallel
or chronologically overlapping. Furthermore, individual or several method
steps or the entire methods may also be carried out repeatedly. For
example, method steps a) to j1) which are described hereinbelow may be
carried out repeatedly individually or as a whole, for example with a
number of repetitions of at least two, a number of repetitions of at
least five and especially preferably a number of repetitions of at least
10 or even at least 20. Furthermore, the methods may also comprise
additional method steps which are not mentioned in the claims.

[0009] In a first aspect, the invention relates to a method for
establishing at least one pattern for at least one pre-defined effector
having at least one determinable effect on a biological system,
comprising the following steps: [0010] a) providing at least one
profile (124) of the pre-defined effector; [0011] b) comparing at least
one value of at least one biomarker of the profile (124), preferably of a
plurality of or all the biomarkers of the profile (124), with at least
one significance threshold in order to ascertain whether the biomarker is
significant; [0012] c) combining significant biomarkers of the profile
(124) to give a pattern.

[0013] By means of this method, it is possible to compile a pattern for a
pre-defined effector, for example a novel effector which has not been
studied as yet, that is, for example, a set of biomarkers which
experience a significant modification when the biological system is
exposed to it.

[0014] Here and hereinbelow, "providing" may, in principle, be understood
as any way of creating the availability of the item to be provided.
Providing may, in particular, be carried out in electronic form, for
example on a volatile or nonvolatile data memory which can be accessed
during the method, so that the item to be provided, in this case the at
least one profile of the pre-defined effector, is available. As an
alternative or additionally, providing may also involve the use of, for
example, a database. However, other types of providing are also possible
in principle. Thus, for example, providing may also be carried out
manually by a user, for example by manual entry in a computer or in the
form of another type of manual provision. Providing may be performed
actively, so that the item to be provided is actively supplied to the
method, or, alternatively, also passively, so that merely an availability
is ensured, for example a retrievability of the data.

[0015] Furthermore, a "biological system" is, within the context of the
present invention, understood as meaning a system which comprises one or
more organisms. If a plurality of organisms are provided, then these may
be arranged in particular spatially connected and include a common
metabolism. The organisms may be of the same type or else different.
Possible nonlimiting examples of biological systems which may be
mentioned are mammals, especially preferably mammals which are capable of
being kept under controlled conditions, such as, for example, dogs, cats,
mice or rats, with rats being especially preferred. Suitable methods for
keeping for example mammals under controlled conditions are from
WO2007/014825. Others which may be mentioned by preference are cell
cultures and plants, in particular plants capable of being grown under
controlled conditions in a greenhouse, such as, for example, Arabidopsis
thaliana or rice.

[0016] Within the context of the present invention, a "metabolite" is
understood as meaning in general intermediates of a metabolic process, in
particular of a biochemical metabolic process. "Metabolism" refers to all
the metabolic pathways of the biological system. Metabolites in the
context of the invention are small molecules (known as "small molecule
compounds"), such as substrates for enzymes of metabolic pathways,
intermediates of such pathways, or their end products. Metabolic pathways
are well known in the prior art and may vary between different species.
Preferred are metabolic pathways at least of the citric-acid cycle, the
respiratory chain, photosynthesis, photorespiration, glycolysis,
gluconeogenesis, the hexose monophosphate pathway, the oxidative pentose
phosphate pathway, the synthesis and the β-oxidation of fatty acids,
the urea cycle, the biosynthesis of amino acids, the biosynthesis of the
nucleotides, nucleosides and nucleic acids (including tRNAs, microRNAs
(miRNA) or mRNAs), protein degradation, nucleotide degradation,
biosynthesis or degradation of lipids, polyketides (including the
flavonoids and isoflavonoids), isoprenoids (including the terpenes,
sterols, steroids, carotenoids or xanthophylls), of the carbohydrates, of
the phenylpropanoids and their derivatives, of the alkaloids, of the
benzenoids, of the indoles, of the indole-sulfur compounds, of the
porphyrins, of the anthocyanins, of the hormones, of the vitamins, of the
cofactors such as prosthetic groups or electron carriers, of the lignins,
of the glucosinolates, of the purines or of the pyrimidines. Accordingly,
metabolites preferably belong to the following groups or classes of
molecules: alcohols, alkanes, alkenes, alkynes, aromatic substances,
ketones, aldehydes, carboxylic acids, esters, amines, imines, amides,
cyanides, amino acids, peptides, thiols, thiol esters, phosphate esters,
sulfate esters, thioethers, sulfoxides, ethers or their derivatives, or
combinations of these. Metabolites may be primary metabolites, that is to
say those which are required for the normal (physiological) function of
the organism or of the organs. However, metabolites also comprise
secondary metabolites which have an essentially ecological function, i.e.
metabolites which allow the organism to adapt itself to the environment.
Besides these primary and secondary metabolites, however, metabolites
also comprise other, in some cases artificial, molecules. These are
derived from exogenous molecules, which are, for example, taken up as
active substances and can then be modified further in the metabolism.
Metabolites may furthermore be peptides, oligopeptides, polypeptides,
oligonucleotides and polynucleotides such as RNA or DNA. Especially
preferably, metabolites have a molecular weight of from 50 da (daltons)
to 30 000 da, more preferably less than 30 000 da, less than 20 000 da,
less than 15 000 da, less than 10 000 da, less than 8000 da, less than
7000 da, less than 6000 da, less than 5000 da, less than 4000 da, less
than 3000 da, less than 2000 da, less than 1000 da, less than 500 da,
less than 300 da, less than 200 da, less than 100 da. Preferably, a
metabolite in the context of the invention will, however, have a
molecular weight of from approximately 50 da to approximately 1500 da.

[0017] Within the context of the present invention, an "effect" may, in
principle, be understood to mean any change, capable of being determined,
of at least one state of the biological system. In particular, this state
may be a biological and/or biochemical and/or chemical state of the
biological system. For example, this effect may be manifested by a change
of a metabolome of the biological system. An effect in the context of the
present invention may preferably be a change in cell morphology, in the
genome, in the metabolome (in other words, in the qualitative or
quantitative state of the metabolites in an organism or a subgroup
thereof), in the proteome (in other words, in the qualitative or
quantitative state of the proteins in an organism or subgroup thereof),
in the transcriptome (in other words, in the qualitative or quantitative
state of the transcripts in an organism or subgroup thereof), in the
organ function, in the cell, tissue or organ vitality (toxicity) and/or
in the psychological or social condition. It is to be understood that
different effects may occur together in the context of the invention.
Thus, a person skilled in the art is familiar with the fact that changes
in cell morphology, in the genome, metabolome, proteome and/or
transcriptome can induce changes in organ function, or may even influence
the psychological or social condition of an organism. As a rule, it is
desirable to detect effects such as organ damage or else psychological
damage of organisms at an early point in time. To this end, it is
especially preferred for the preceding modification to provide an
indicator. With the aid of the modifications of the metabolome, it is,
therefore, possible to predict organ dysfunctions or other damage.
Naturally, positive effects such as the healing of specific diseases,
yield-increasing properties in the cultivation of useful plants, or
ecological damage in micro ecosystems may likewise be predicted in
advance. Toxicological, pharmacological or bioenvironmental risk
stratification of different effectors allows better control of the
beneficial use or dealings with these effectors and maximal avoidance of
harmful use or dealings with them.

[0018] Within the scope of the present invention an "effector" is
understood as meaning, in principle, any influence on the biological
system that might potentially have at least one effect, which may, in
principle, be of any type, on the biological system. This potential
effect may, in particular, be an effect of the abovementioned type, in
particular a biochemical and/or biological and/or chemical effect, which
might, in particular, be manifested by a change in the metabolism.
Examples of such influences are exposure of the biological system to one
or more chemical substances and/or compounds such as, for example,
medicaments and/or pesticides, and/or physical action on the biological
system, for example exposure of the biological system to electromagnetic
radiation and/or particle radiation. A different duration and/or
intensity and/or dose of the effect on the biological system may also be
visualized by suitable effectors within the scope of the present
invention. For example, different durations and/or intensities and/or
doses of one and the same influence on the biological system may be
considered to be different effectors. If, for example, an effector
includes an exposure of the biological system to at least one chemical
substance and/or chemical compound and/or to at least one radiation, then
for example different durations and/or different intensities and/or
different doses of this exposure may be considered to be different
effectors. In this context, the duration and/or dose and/or intensity may
also be graded into two or more levels. For example, when exposing the
biological system to at least one chemical substance and/or chemical
compound and/or to at least one radiation, a low dose and a high dose to
which the biological system is exposed, as desired, will be
predetermined, with exposure to the low dose and exposure to the high
dose being considered to be two different effectors.

[0019] An effector may be used individually or in cooperation with other
effectors so that for example a group of effectors acts together.
Preferred effectors within the scope of the invention are chemical
substances, pharmaceutical active substances and potential pharmaceutical
active substances (candidate active substances), pesticides (herbicides,
insecticides or fungicides), growth promoters, for example fertilizers,
radiation treatments, modifications of the genetic material, for example
in the form of random or deliberately generated mutations in the genome
of an organism or by integration of genetic material via recombinant
methods, and/or changes in environmental conditions (temperature,
radiation, nutrition, water balance; gas composition and pressure of the
surrounding atmosphere and the like).

[0020] Within the scope of the present invention, a "biomarker" generally
refers to a state of a metabolite or of a specific group of metabolites.
This state may be dependent in particular on constraints and/or
parameters, for example age of the biological system, time of recording
of the state, in particular a period after exposing the biological system
to at least one effector, and optionally further information on the
biological system, for example a sex. Thus, for example, a specific level
of a metabolite or a particular group of metabolites may be specified as
a biomarker as a function of a sex of the biological system and/or a
point in time. As an alternative or in addition, a biomarker may also
describe a state change of a metabolite or a specific group of
metabolites, for example again as a function of constraints and/or
parameters, for example of the abovementioned type. A distinction must be
made between the biomarker itself, as a variable quantity, and its
numerical value, with the aid of which, for example, it is possible to
determine whether a biomarker is significant or not. As explained in
greater detail hereinbelow, this determination of whether a biomarker is
significant or not may, for example, be carried out by comparing its
numerical value with one or more significance thresholds. A biomarker
may, in principle, be expressed in any unit, for example in absolute
units or in relative units, for example as a change relative to a
reference value, in particular a normal state and/or a state in which the
biological system is not exposed to the effector and/or the group of
effectors and/or any effector in the first place.

[0021] A "profile" of a specific effector or group of effectors, in some
cases also referred to as metabolic profile, is understood as meaning, in
the context of the present invention, the totality of biomarkers which
are recorded or have been recorded or can be recorded during or after
exposure of the biological system to the effector. It therefore takes the
form of a total set of biomarkers which can also be recorded, or of a
subset of this total set which is taken into account for a specific
study. This totality is preferably recorded under controlled and
standardized conditions during or after exposure of the biological system
to the effector or the group of effectors. For example, this profile may
be a metabolome, or subset of the metabolome, caused by exposing the
biological system to the at least one effector, or may comprise a
metabolome or subset of a metabolome. A profile of metabolites may
preferably be determined by methods which allow both quantitative and
qualitative determination of the metabolites in the organism. To this
end, a sample from the organism, which sample comprises a representative
extract of the metabolites, may be analyzed. Suitable sample materials
include bodily fluids such as blood, serum, plasma, urine, saliva, fecal
matter, tear fluid, secretions or liquor, or tissue samples obtained via
biopsy. Naturally, samples may also be samples from a micro ecosystem or
from cultured cells. Samples may also be pretreated, for example to
obtain a subcellular fraction (nuclei, endoplasmic reticulum,
photosystem, peroxisomes, Golgi apparatus and the like) as the actual
sample. The metabolic profiles of such samples can be obtained preferably
by mass spectrometry techniques, NMR or other of the methods mentioned
hereinbelow. Mass spectrometry techniques may generally be understood as
meaning analyses of samples using mass spectroscopes and/or mass
spectrometers, in particular mass separation techniques in which the ions
are analyzed by photosensitive detectors. Mass spectrometry techniques
and mass spectroscopy techniques can be used equally in the context of
the present invention.

[0022] Before the actual qualitative and/or quantitative determination of
the metabolites the latter may initially be separated further, which
facilitates said determination, especially in the case of samples with a
complex composition. To this end, it is possible to employ separation
methods which are well known in the prior art. These are, preferably,
chromatography-based techniques such as "liquid chromatography (LC)",
"high performance liquid chromatography (HPLC)", gas chromatography (GC),
thin-layer chromatography, size-exclusion chromatography or affinity
chromatography. However, it is most preferred to employ LC and/or GC.

[0024] The methods mentioned hereinabove are particularly suitable for
determining the states of a multiplicity of metabolites in samples and
therefore for recording the values of the characteristics required for
compiling the profile. The methods preferably provide a value for an
identity parameter and one or more values for one or more parameters
induced by the physical, chemical or biological properties of the
measured metabolites. The biomarker profile can therefore include not
only values which allow the chemical nature of the metabolites to be
determined, but also a value capable of reflecting quantitative changes
in the metabolites, in other words the amount measured in a particular
sample. The methods mentioned hereinabove are also suitable for
high-throughput analyses, so that different samples can be measured in an
automated manner at short time intervals, and it is possible to compile a
multiplicity of profiles, capable of being compared with each other,
within a short time.

[0025] A "pattern" of a particular effector or group of effectors within
the scope of the present invention is intended to mean a set of
biomarkers which exhibit a significant change when the biological system
is exposed to a particular effector or a particular group of effectors.
For example, the biomarkers may be specified in the profile itself, or
their values in absolute values and/or in relative values and/or in
changes, for example rates of changes or changes in comparison with at
least one normal value. The change may also be viewed in absolute values
and/or in relative units, for example in comparison with at least one
reference value and/or a normal state, in particular a state in which
there is not, or has not been, any exposure to the effector and/or group
of effectors and/or any effector.

[0026] What must be regarded as significant in this context depends on the
individual case and can be preselected for example by a user and/or an
evaluation device, and/or adjusted manually. In this context, for
example, it is possible to pre-define one or more threshold values, also
referred to hereinbelow as significance thresholds, for example one or
more threshold values for one or more biomarkers and, in particular, for
each biomarker or for each group of biomarkers a significance threshold,
with the aid of which it is possible to decide whether a change is
significant or not. These threshold values may also be variable in this
context and, for example, adapted iteratively in order to adjust the
sensitivity and/or selectivity.

[0027] Within the scope of the present invention, "selectivity" is
generally understood as meaning the ability or property of systematically
selecting specific elements from a total number of possible elements. The
selectivity may therefore represent a measure of the narrowness of the
selection. If, as is also possible within the scope of the present
invention, a database is used, then the selectivity may be a measure of
proportion of the selected elements in the total content of the data of
the database, in particular for a database search by means of an index.
Thus, for example, the selectivity may specify the number of biomarkers
which are selected from the total number of biomarkers with the aid of
the changes and assigned to the pattern. A high selectivity, for example
owing to a high threshold value, generally leads to a lower number of
biomarkers in the pattern, and a low selectivity, for example owing to a
low threshold value, generally leads to a higher number of biomarkers in
the pattern.

[0028] Within the scope of the present invention, "sensitivity" generally
means the likelihood that an event which is genuinely positive will
indeed be identified by a positive test result. For example, the
sensitivity may represent a measure or a likelihood of whether a change
of a particular biomarker can indeed be attributed to exposure to an
effector or group of effectors, or whether the change is a random change,
a measurement error or a noise or any other interfering influence. For
example, the sensitivity may also describe a true-positive rate, a
sensitivity or a hit ratio. The sensitivity may in particular be the
proportion of events which are correctly identified as positive out of
the total number of the events which are indeed positive, i.e. for
example a proportion of the biomarkers with a change which has correctly
been considered to be significant out of the total number of biomarkers
which should exhibit a significant change due to exposure to the effector
or group of effectors. A high sensitivity, for example owing to a low
threshold value, generally leads to a high number of biomarkers in the
pattern, while a low sensitivity, for example owing to a high threshold
value, generally leads to a low number of biomarkers in the pattern.

[0029] The method according to the invention described hereinabove for
establishing at least one pattern for at least one pre-defined effector
having at least one determinable effect on a biological system permits
the rapid convolution of extensive biological data. With the aid of the
threshold values to be pre-defined, the most relevant biomarkers can be
ascertained rapidly and reliably from a pre-defined profile. The method
can also be carried out readily by computer implementation and may
therefore be used in particular with the other method elements in
high-throughput analysis. The patterns obtained by the method may be used
in the applications discussed elsewhere in the description, and they
allow a simplified and more efficient analysis of biological data sets.

[0030] In a preferred embodiment, the method according to the invention
furthermore comprises the following steps: [0031] d) providing a
database in which profiles are stored for a multiplicity of further
effectors; [0032] e) establishing at least one further pattern of at
least one further effector of the database.

[0033] In this manner, for example, an already existing database may be
evaluated. By means of a multiplicity of measurements, for example, a
database with profiles of different effectors can be collected. At least
some of these effectors may, for example, be known effectors, in other
words for example effectors whose effects on the biological system are
known, for example their toxic effects. Within this database, an
evaluation may be carried out by establishing profiles for one or more of
the effectors.

[0034] In another preferred embodiment of the method according to the
invention, it also comprises the following step: [0035] f) the at least
one further pattern established in method step e) is compared with the
pattern established in method step c).

[0036] In this manner, for example, a comparison may be carried out for
the pre-defined effector with the at least one further effector of the
database, for example with at least one further effector which is already
known. Thus, for example, a search may be carried out for similar
effectors.

[0037] In a comparison of specific objects, various methods may generally
be subsumable within the scope of the present invention. A comparison of
two patterns may, for example, be carried out as to whether the patterns
comprise the same biomarkers.

[0038] The result of this comparison may, for example, consist in a
characteristic quantity which describes the degree of the match. This may
be, for example, a percentage, for example 100 percent, if the first
pattern consists of the same biomarkers as the second pattern.
Correlation information or similar information may also be used in order
to characterize the degree of the match.

[0039] Furthermore, as an alternative or in addition, it is also possible
to compare the values of the patterns or at least to include the values
in the comparison. Thus, it is possible to check not only whether the
patterns comprise the same biomarkers, but also whether and optionally to
what extent the values of these matching biomarkers coincide. Again, this
can be done for example by using a correlation function or similar
mathematical methods. Furthermore, it is also possible to simply
pre-define one or more similarity thresholds so that for example a
percentage deviation of the two values from each other which is above a
pre-defined threshold is considered to be a non-match, and a percentage
deviation of the values from each other which is below the threshold is
considered to be a match. Other comparison methods may also be envisaged.

[0040] In another preferred embodiment of the method according to the
invention, it also comprises the following steps: [0041] g) checking
whether the at least one further effector has at least one known effect;
[0042] h) comparing the at least one known effect with the previously
determined determinable effect of the pre-defined effector.

[0043] This method variant thus relates to the actual comparison of the
effects of the pre-defined effector, which must then also actually be
determined or known in another way, with the known effect of the further
effector.

[0044] For a comparison of the effects, in turn, it is possible to employ
a variety of methods. Thus, for example, it is possible to categorize
effects. In this way, for example, effects may readily be specified
digitally, for example the effect is "hepatotoxic". The effects may
additionally be quantified further, for example in graded information
such as "highly hepatotoxic", "averagely hepatotoxic" or "weakly
hepatotoxic". Other quantifications may also be found. Thus, it is
possible in turn to give a quantitative expression by quantifying the
degree of the match instead of a simple digital expression "effect
matches" or "effect does not match". Again, this may be done, in turn,
using known mathematical methods, for example by assigning numerical
values to the graded information so that, for example, percentages or
other quantitative information may in turn be used as an expression for
the degree of the match.

[0045] If purely digital expressions of the type "effect matches" or
"effect does not match" (for example "both effectors are hepatotoxic" or
"one of the effectors is hepatotoxic, while the other one is not") are
given, then further evaluation is comparatively simple. If, however,
intermediate values are allowed which categorize the degree of the match,
then the operation may be carried out in turn with a threshold value
method. Thus, for example, the degree of the match of the effects may be
compared with one or more threshold values. If the degree of the match
exceeds the threshold value, it can be assumed that the effects match,
while a non-match may be assumed if the degree of the matches lies below
the threshold value.

[0046] After this method has been carried out, there are several options.
The starting point is found, as a rule, by the objectively determinable
effects. If the effects match, while the patterns do not, then the
pattern determination has not delivered the desired result and must
optionally be improved. If, however, the effects match then the pattern
comparison and the comparison of the effects deliver the same result, and
the determination and comparison of the patterns therefore represent a
successful way of comparing effects of different effectors or, for
example, of predicting effects of unknown new effectors.

[0047] Accordingly, in another preferred embodiment of the method
according to the invention, it also comprises the following step:
[0048] i) if an at least partial match of the patterns is found in method
step f), carry out the following step: [0049] i1) if no match is found in
method step h): changing the significance threshold, in particular
increasing the significance threshold, and repeating at least method
steps b) and c).

[0050] This method variant represents an improvement in the pattern
generation by correspondingly refining the algorithm. In other words,
this method variant describes the case in which, although there is an at
least partial match of the previously generated patterns (for example
according to the description above a one hundred percent match, a match
above a pre-defined threshold or a match by at least a pre-defined
threshold), in fact no effect match or merely a minor effect match (for
example below a pre-defined threshold) can be found for these effectors
which, according to the ascertained patterns, should have an at least
partial match of the effects (for example again by at least one
pre-defined degree or by more than a pre-defined degree). In other words,
this may comprise the case that, while the patterns match, the effects do
not.

[0051] This is an indicator that the patterns have been selected
incorrectly. For example, this may be attributable to biomarkers, whose
values exhibit more of a random match, having been erroneously selected
for the pattern even though these biomarkers do not represent suitable
indicators for the suitable effect. This may for example be the case when
the above-described threshold values in the pattern generation, in
particular in method steps b) and c), have been selected unduly low for
all the biomarkers, for a few biomarkers or for individual biomarkers.
Correspondingly, according to the proposed method variant, a full or
partial repetition of the pattern generation may be carried out. For
example it is possible to automatically or manually increase all, a few
or individual significance thresholds, so as to exclude from the patterns
biomarkers which do not represent a suitable indicator for the effect.

[0052] In a preferred method variant, this can be carried out in
particular in such a way that method steps i) and i1) are carried out
repeatedly with a stepwise increase of the significance threshold.

[0053] Furthermore, the case may arise that no match of the patterns is
found, even though a match of the effects can be found. This may mean in
particular that biomarkers which would in fact have been suitable
indicators of the effect have been excluded from the pattern generation
by the threshold value method in method steps b) and c), for example
because the threshold values were set too high.

[0054] In another preferred embodiment of the method according to the
invention, it also comprises the following step: [0055] j) if no match
of the patterns is found in method step f), carry out the following step:
[0056] j1) if a match is found in method step h): changing the
significance threshold, in particular reducing the significance
threshold, and repeating at least method steps b) and c).

[0057] This in turn means that the pattern generation may be refined by
adapting the threshold values. In this case, a method which is especially
preferred is one in which method steps j) and j1) are carried out
repeatedly with a stepwise reduction of the significance threshold.

[0058] The above-described method in one of the configurations described
may be used in particular to group effectors according to their effect.
In another aspect, therefore, the invention relates to a method of
establishing a class of effectors for a pre-defined effect or group of
effects. The method is based on the method presented above for
establishing at least one pattern and comprises this method as a key
component. The method comprises the following steps: [0059] A)
specifying at least one effector which is presumed to be assigned to the
class of effectors, and assignation to the class of effectors; [0060] B)
establishing or updating at least one pattern of the at least one
effector, in particular by using the method for establishing a pattern in
one of the configurations described above; [0061] C) providing a database
in which profiles are stored for a multiplicity of further effectors;
[0062] D) searching for effectors with identical or similar profiles in
the database; [0063] E) assigning the effectors determined in step D) to
the class of effectors.

[0064] In method step B), it is preferred to use a method of establishing
a pattern in one of the configurations described above. In principle,
however, other methods of establishing patterns may also be used, or
already known patterns may be employed. For example, specific patterns of
effectors of a particular effect are by now known from the literature in
some cases, since, for example, it is known that specific effectors have
an effect on specific metabolites.

[0065] A "class of effectors" within the scope of the present invention is
understood as meaning a set of effectors which have the same known effect
on the biological system or at least a similar effect on the biological
system. The starting point for establishing a class of effectors is
therefore a particular effect on the biological system, or a combined
group of effects. The class of effectors may be a set of effectors which
have at least one specific effect on the growth and/or functionality of
the biological system, for example a specific toxic effect and/or a
specific curative effect. A class of effectors may, in principle, first
be configured as an empty set and then for example be added to later so
that it preferably comprises at least one effector, in particular a
plurality of effectors. As will be explained in greater detail below, a
class of effectors may optionally also be established beforehand, for
example firstly by at least one effector suspected of having the specific
effect or the group of effects being assigned to the class of effectors.
Thereafter, it is possible to supplement the class of effectors with one
or more further effectors, as explained in greater detail hereinbelow,
for example iteratively.

[0066] According to the possible effects, the class of effectors may
comprise chemical compounds which mediate specific effects, for example
organ toxicity, tissue toxicity or cell toxicity, optionally according to
a specific molecular mode of action. For a toxicological risk
stratification, it is helpful to know the precise mode of action for
compounds. The effect mediated by a class of effectors may also be a
pharmacological effect. Again, early categorization of an active
substance is helpful for further pharmacological classification and
allows early risk stratification so that unsuitable candidate active
substances can promptly be eliminated before the start of clinical
studies. Likewise, the effect mediated by a class of effectors may be, or
comprise, a herbicidal, fungicidal or insecticidal effect or any
combination of these effects. Again, early categorization of an active
substance is helpful in the further classification and makes possible
early risk stratification so that unsuitable candidate active substances
can promptly be eliminated before the start of further studies. Genetic
modifications as effectors may likewise form classes of effectors. Thus,
for example, yield-increasing genetic modifications, or genetic
modifications which increase pest resistance, may be combined in in each
case one class of effectors. The method according to the invention
preferably makes it possible to compile and collate effectors to form a
class of effectors on the basis of the individual patterns of the
individual effectors. The effectors of a class of effectors here
preferably have essentially identical patterns. On the basis of these
considerations, the method according to the invention for establishing a
class of effectors makes possible said establishing of the class of
effectors.

[0067] A method in which all or at least one of steps B) to E) is/are
carried out repeatedly is preferred.

[0068] In a preferred embodiment of the method according to the invention,
expert knowledge is employed in step A). For example, it is possible to
employ the knowledge of an expert, for example a toxicologist, to
pre-define at least one effector which is known to have a pre-defined
effect, for example a pre-defined toxic effect.

[0069] The possibility of collating classes of effectors may furthermore
be used to make predictions about at least one effect of at least one
new, at least as yet not fully known effector and/or in order to
determine at least one effect of an effector. In this context, it is
possible in particular to use one or more classes of effectors which may
have been established by the method of establishing a class of effectors
for a pre-defined effect or group of effects according to one or more of
the configurations described hereinabove. In principle it is also
possible, as an alternative or in addition, however, to use classes of
effectors obtained in other ways. Thus, particular classes of effectors
are in turn known from the literature since, for example, the effects of
many effectors are catalogued so that effectors with the same effect can
be grouped.

[0070] Accordingly, in another aspect, the invention also relates to a
method for identifying at least one effect of a pre-defined effector,
comprising the following steps: [0071] i) establishing at least one
class of effectors for at least one known effect, in particular by a
method according to the invention of establishing a class of effectors as
described hereinabove; [0072] ii) establishing at least one pattern of
the pre-defined effector, in particular by one of the methods according
to the invention for establishing a pattern as described hereinabove;
[0073] iii) comparing the pattern established in step ii) with the
pattern of the class of effectors established in step i).

[0074] Identification of at least one effect may, in this context,
generally be understood as ascertaining a result that the pre-defined
effector has at least one particular, specifically indicated effect. As
an alternative or in addition, the at least one effect may also be
identified, which is likewise to be understood by identifying at least
one effect, by carrying out a comparison with at least one other effector
and, accordingly, [0075] equating the effect of the pre-defined
effector with at least one effect of the further effector, [0076]
identifying the effect of the pre-defined effector as being similar to
the at least one effect of the further effector, or [0077] identifying
the effect of the pre-defined effector as being different from the at
least one effect of the further effector.

[0078] Thus, for example, it may be ascertained that the pre-defined
effector has at least one equal, similar or dissimilar effect to the at
least one further effector with which the comparison is carried out.

[0079] In a preferred embodiment of the above method according to the
invention, if a match or a similarity is found in step iii), the known
effect of the class of effectors is equated with the effect to be
ascertained of the pre-defined effector. This means that the effector in
question, whose effect is to be ascertained, may in particular have the
same effect as the class of effectors employed for the comparison, whose
effect is in fact known. In this way, it is efficiently possible to
quickly obtain at least one provisional estimate of the effect of this
effector while reducing laboratory experiments to one unknown effector.
This allows considerable research costs savings and it also makes it
possible to reduce for example animal experiments to a minimum.

[0080] The method described hereinabove may, in principle, also be carried
out without using a class of effectors, and methods using a class of
effectors and methods without using a class of effectors may also be
combined. If the route via at least one class of effectors is not
selected, or at least not exclusively selected, it is possible also to
resort directly to for example the ascertained patterns. Here, in
particular, it is possible again to employ one or more patterns which
have been obtained by means of the method of establishing at least one
pattern according to one or more of the configurations mentioned
hereinabove. Alternatively or as an addition, however, it is also
possible in turn to employ one or more patterns which have been obtained
in another way or which are known for example from the literature.

[0081] In a further aspect, therefore, the invention also relates to a
method for identifying at least one effect of a pre-defined effector,
comprising the following steps: [0082] I) establishing at least one
pattern of the pre-defined effector, in particular by one of the
above-described methods according to the invention for establishing a
pattern; [0083] II) providing a database in which profiles are stored for
a multiplicity of further effectors; [0084] III) searching in the
database for effectors with a similar or identical pattern to the pattern
established in step I); [0085] IV) checking whether the effectors
ascertained in step III) have at least one known effect; [0086] V) if a
known effect is found in step IV), equating the effect of the pre-defined
effector with the known effect.

[0087] As mentioned hereinabove, this method may in principle also be
combined with the method in which the route via the at least one class of
effectors is chosen. In both cases, effects of effectors can at least
provisionally be predicted rapidly and reliably by comparison with known
effectors, whether they now be grouped according to classes of effectors,
or be individual.

[0088] The methods described hereinabove may be carried out fully or in
part by means of a computer, or else they may be carried out at least in
part using a computer. In particular, it is possible for one or more of
the following method steps to be carried out by using a computer: a), b),
c), d), e), f), g), h), i), i1), j), j1), A), B), C), D), E), i), ii),
iii), I), II), Ill), IV), V), VI).

[0089] The invention therefore furthermore comprises a computer program
having a program code for implementing the method according to any of the
preceding method claims when the program is run in a computer. The
computer program may be adapted to carry out, or at least assist, one or
more or all of the method steps. In particular, all of method steps a) to
c), all of method steps A) to E), all of method steps i) to iii) or all
of method steps I) to VI) may be carried out by using at least one
computer or computer network or using the computer program.

[0090] The computer program may in particular be configured as a saleable
product. The computer program according to the invention is preferably
stored on a machine-readable medium.

[0091] The invention furthermore comprises a computer, adapted to carry
out a method according to any of the preceding method claims. The
computer may generally comprise at least one dataprocessing device and/or
one computer network.

[0092] Finally, the invention also relates to a data medium on which a
data structure is stored, which carries out the method according to any
of the preceding method claims, after loading in a working and/or main
memory of a computer or computer network.

[0093] The proposed methods, the computer program, the computer and the
data medium have many advantages over methods and devices known from the
prior art, some of which have already been mentioned hereinabove. Thus,
it is possible in particular to ascertain relationships and make
predictions in very confusing experimental data sets. The data, for
example raw data with measurement values for biomarkers of a multiplicity
of different effectors, can be evaluated and categorized efficiently in
this manner, and new types of presentation and/or representation may be
found (for example in the form of patterns and/or classes of effectors),
and/or data sets can be reduced considerably. Furthermore, owing to the
possibility of predicting effects of previously unknown or only
insufficiently known effectors, the experimental workload and the time
for screening a multiplicity of new effectors can be reduced
considerably.

BRIEF DESCRIPTION OF THE FIGURES

[0094] Other possible details and features of the invention may be found
in the following description of preferred exemplary embodiments. Some of
the exemplary embodiments are represented schematically in the figures.
Reference numbers which are the same in different figures refer to
elements which are the same or functionally the same or correspond to one
another in their function. The invention is not restricted to the
exemplary embodiments.

[0095] In detail:

[0096] FIGS. 1A-1B

[0097] show an exemplary embodiment of a method according to the invention
for determining a pattern of an effector;

[0098] FIGS. 2A-2D

[0099] show an exemplary embodiment of a method according to the invention
of establishing a class of effectors;

[0100] FIGS. 3A-3B

[0101] show a first exemplary embodiment of a method for identifying at
least one effect of a pre-defined effector;

[0102] FIGS. 4A-4B

[0103] show a second exemplary embodiment of a method for identifying an
effect of a pre-defined effector; and

[0104] FIGS. 5A and 5B

[0105] show a use example of the method according to FIGS. 4A and 4B for
comparing effects of two chemically similar substances.

EXEMPLARY EMBODIMENTS

[0106] FIGS. 1A and 1B represent an exemplary embodiment of a method
according to the invention for establishing at least one pattern for at
least one pre-defined effector having at least one effect on a biological
system, which effect is capable of being determined. In this context,
FIG. 1 shows a schematic flow chart of this method, while FIG. 1B shows a
screen capture of an exemplary screen representation. The two figures
will be described together hereinbelow.

[0107] In FIG. 1A, reference number 110 denotes the provision of at least
one profile of the pre-defined effector, reference 112 denotes the method
step of comparing at least one value of at least one biomarker of the
profile with at least one significance threshold in order to ascertain
whether the biomarker is significant, and reference number 114 denotes
the combining of significant biomarkers of the profile to form a pattern.

[0108] This method is represented by way of example in FIG. 1B. It shows a
possible representation of a database 116 which is provided for the
method according to the invention and from which an evaluation may be
carried out by means of appropriate software, which implements the
proposed method.

[0109] FIG. 1B represents various metabolites 118 in the column of a table
headed "Metabolites", the values or changes of which being monitored
while exposing the biological system, in this case a rat, to various
effectors 120. The metabolites 118 may optionally be selected from a list
of possible metabolites by means of appropriate markings (column
"Select").

[0110] Various biomarkers 122 are recorded for each metabolite 118 and are
specified in the rows following the metabolites 118. For example, the
absolute values or changes of a particular metabolite 118 may be recorded
for male (m) and female (f) test subjects (for example rats).
Furthermore, biomarkers 122 may be recorded for exposure of the test
subjects to a low dose (l) and for exposure to a high dose (h).
Furthermore, the absolute values or changes of the metabolites 118 may be
recorded after a pre-defined duration, for example after a duration in
days, for example after 7 days (7), after 14 days (14) or after 28 days
(28). Thus, for example, the biomarker 122 in the row allocated to the
metabolite threonine in column ml7 denotes the absolute value or the
change of the metabolite threonine when a male test subject (m) is
exposed to a low dose (l) for measurement 7 days after exposure of the
test subject to the effector 120, for example a substance with the
designation "compound 1" or a substance with the designation "compound
2". Each metabolite 118, therefore, has assigned to it a multiplicity of
biomarkers 122. The total number of biomarkers 122 of a particular
effector 120 is also referred to as profile 124. FIG. 1B represents
profiles 124 or parts of these profiles 124 for the two effectors 120
compound 1 and compound 2, the profile for compound 1 being denoted by
the reference number 126 and the profile for compound 2 being denoted by
the reference number 128, by way of example.

[0111] Thus, FIG. 1B firstly shows the method step 110 of FIG. 1A of
providing profiles 124, in this case optionally for a plurality of
effectors 120, in this case by means of a database 116 and a
corresponding option for representing, grouping and/or evaluating the
biomarkers 122 comprised in this database 116, using a computer program.

[0112] Furthermore, on the other hand, FIG. 1B also shows indicatively the
method step 112 mentioned in FIG. 1A of comparing the values of the
biomarkers 122 with corresponding significance thresholds. For each
biomarker 122 and/or for each metabolite 122, for example, one or more
significance thresholds may be specified, which may also be capable of
being influenced by a user. Thus, by way of example, in the table in
which the profiles 124 are reproduced, biomarkers 122 which have a
significantly increased value, that is for example an increase in the
value of these biomarkers 122 above a significance threshold, are marked.
Furthermore, although this cannot be seen from FIG. 1B, significantly
reduced biomarkers 122 may be highlighted as an alternative or in
addition, for example by means of a different color. For example, the
column which is headed "Direction" may specify the principal direction of
the change of the biomarkers 122 for each of the metabolites 118.

[0113] By means of this evaluation, the method step denoted hereinabove by
the reference number 114 of the method according to FIG. 1A may be
implemented. For example, all the biomarkers 122 of a profile 124, which
exhibit a significant increase and/or a significant reduction, may be
combined to form a pattern. All the biomarkers 122 with a gray background
of the profile 128 for compound 2, which exhibit a significant increase,
and/or all the biomarkers 122 not marked in FIG. 1B for compound 2, which
exhibit a significant reduction, may be combined for example to form a
pattern for compound 2. It should be pointed out that this pattern then
comprises the biomarkers 122 for this effector 120, but preferably not
the values of these biomarkers 122. Thus, the pattern for compound 2 in
the exemplary embodiment shown in FIG. 1B comprises for example the
biomarkers threonine ml7, threonine m114, threonine m128, threonine mh7,
threonine mh14, threonine mh28, glycine m17, glycine m114 and the like,
in other words the values shown with gray backgrounds in FIG. 1B of the
table part to be allocated to the pattern 128, but not the numerical
values entered in the fields of this part of the table.

[0114] In this manner, it is thus possible for example to establish a
pattern for a pre-defined effector 120. This establishing may optionally
also be carried out iteratively as described above, for example by
interactive adaptation of threshold values. The pattern is symbolically
denoted in FIG. 1B by the reference number 130. This symbolic reference
number 130 will no longer be shown in the subsequent figures, so that in
respect of this reference number reference may be made for example to
FIG. 1B.

[0115] FIGS. 2A-2D show an exemplary embodiment of a method according to
the invention of establishing a class of effectors for a pre-defined
effect or group of effects by way of example. Here, FIG. 2A shows a
schematic flow chart of an exemplary embodiment of the method according
to the invention, FIG. 2B shows an iterative method variant, and FIGS. 2C
and 2D show, in turn, screen representations of an exemplary embodiment
of the method in various stages, using a database 116. The figures will
again be explained together hereinbelow.

[0116] In FIG. 2A, the reference number 210 denotes a method step in which
at least one effector 120 is specified which is likely to be assigned to
the class of effectors to be established. This effector 120 is assigned
to the class of effectors to be established.

[0117] In FIG. 2A, method step 210 is followed by a method step 212, in
which at least one pattern of the at least one effector of the class of
effectors is established. In an iterative method, which is explained with
the aid of FIG. 2B, establishing is understood as meaning not recreating,
but updating, the at least one pattern.

[0118] In a further method step which is likewise represented in FIG. 2A
and denoted by the reference number 214, a database 116 is provided,
where a multiplicity of further profiles 124 of effectors 120 are stored
in the database 116.

[0119] In a method step denoted by reference number 216 in FIG. 2A, a
search is made in the database 116 for effectors 120 with the same or
similar profiles to those profiles of the effectors 120 which have
already been assigned to the class of effectors. This may be done either
by a direct comparison of the profiles 124 or by using patterns. For
example at least one profile may be established for each, several or at
least one further effector 120 in the database 116, for example a profile
with the same biomarkers 122 as comprised by the at least one pattern
established and/or updated in method step 212 for the effectors of the
class of effectors. With the aid of this pattern comparison it is
possible to ascertain whether the at least one further effector has an
identical or similar profile 124. If, in method step 216, such effectors
120 which have the same or similar profiles to the effectors 120, which
are already assigned to the class of effectors are ascertained, then
these effectors 120 can be assigned to the class of effectors in a method
step 218.

[0120] The method described in FIG. 2A may be carried out in particular
iteratively. This is shown in FIG. 2B. There, again, in method steps 210
and 212, one or more effectors 120 which are likely to be assigned to the
class of effectors to be established are initially specified, for example
starting with at least one effector 120. The class of effectors is
denoted by the reference number 220 in FIG. 2B.

[0121] One or more patterns are then determined in method step 212 for
this class of effectors 220, which can initially be considered to be a
provisional class of effectors 220.

[0122] In method steps 214 and 216, a database 116 with further effectors
120 is, in turn, specified, and a search is made in this database 116 for
effectors 120 with the same or similar profiles 124, for example with
identical or similar patterns 130. If this search is successful, then
this at least one further effector 120 that has possibly been ascertained
in this manner is assigned to the class of effectors 220 in method step
218. The method may then, as indicated in FIG. 2B, be carried out again
in order to ascertain further effectors 220 which are to be assigned to
the class of effectors 220.

[0123] This method will be illustrated in greater detail by way of example
with the aid of FIGS. 2C and 2D. Thus, for example, FIG. 2C again shows a
screen representation which explains method steps 210 and 212. This
representation is a mode of representing a part of a database 116.

[0124] In this example, it is desired to establish, by way of example, a
class of effectors 220, which comprises effectors 120 which have an
effect of the peroxisome proliferation type.

[0125] For this peroxisome proliferation effect, a provisional class of
effectors 220 is initially formed which is based for example on expert
knowledge and/or literature information. The expert knowledge consists
for example in that the effectors 120 of the mecoprop-p, fenofibrate and
dibutyl phthalate type have the abovementioned effect. These effectors
120 are therefore assigned to the provisional class of effectors 220, as
shown in FIG. 2C. From the database 116 and/or in another way, profiles
124 are specified for these effectors. These profiles 124, which are
represented in FIG. 2C by way of example and in some cases as excerpts,
again comprise a multiplicity of biomarkers 122. For example, similarly
as in the representation in FIG. 1B, these biomarkers 122 are biomarkers
which are characterized by the sex of the test subject, the level of the
dose and/or the time after exposure of the test subjects to the effector
120, in each case for different metabolites 118. It should be pointed out
that the representation in FIG. 2C is to be taken merely by way of
example and may, in turn, comprise for example an excerpt of examples. In
contrast to the representation in FIG. 1B, the metabolites 118 are merely
specified by numbers.

[0126] Furthermore, biomarkers 122 whose values exhibit a significant
change, in turn, are shown against a gray background in FIG. 2C, in
respect of which reference may be made for example to the description of
FIG. 1B. From this comparison of the biomarkers 122 or their values with
corresponding significance thresholds, a pattern can, in turn, be
compiled. For example, this pattern may comprise biomarkers 122 which
exhibit a significant change in the same direction in all three profiles
124 of the three effectors 120 of the provisional class of effectors 220.
Since, for example, in the case of all effectors 120 for the metabolite
of the "metabolite 45" type, the biomarker 122 with the designation fh7
and the biomarker with the designation fh14 exhibit a significant change,
these biomarkers 122 should preferably be assigned to pattern 130. In
this way, a pattern can be collated for the provisional class of
effectors 220.

[0127] With this provisional class of effectors 220, a search can then be
made in the database 116 for further effectors 120 which are likewise to
be assigned to the class of effectors 220. This is represented by way of
example in FIG. 2D, in a representation similar to FIG. 2C. Biomarkers
122 for a multiplicity of metabolites 118 are again entered in the rows
of the tabular representation. These biomarkers are in turn assigned to
effectors 120. Besides the effectors 120 of the type mecoprop-p,
fenofibrate and dibutyl phthalate which have already been assigned to the
class of effectors 220, the effectors bezafibrate, clofibrate, dicamba
and dichlorprop-p and their associated profiles 124 are indicated as
further effectors 120. With the aid of a comparison of the patterns 130,
which are not explicitly identified in FIG. 2D likewise as in FIG. 2C, it
is possible to ascertain further effectors 120 which have the same or
similar profiles 124, and in particular which have the same or similar
patterns 130. In this manner it is possible to determine, for example by
groups or iteratively, further effectors 120 which are to be assigned to
the class of effectors 220.

[0128] FIGS. 3A and 3B represent a first exemplary embodiment of a method
according to the invention, by means of which at least one effect of a
predetermined effector 120 can be ascertained. Possible effects are
denoted in FIG. 3B by the reference number 310. FIG. 3A in turn shows a
schematic flow chart of a basic form of the exemplary embodiment of the
proposed method, while FIG. 3B shows an example in tabular form in which
a search is made in a database for effects 310 for the effector 120 of
the diethylhexyl phthalate type.

[0129] In the method represented in FIG. 3A, at least one class of
effectors for at least one known effect 310 is initially established in
method step 312. For example, an effect 310 may be specified for which a
class of effectors is determined, for example by the method described
with reference to FIGS. 2A-2D.

[0130] In method step 314, at least one pattern 130 is established for the
pre-defined effector 120, whose effect 310 is to be ascertained.

[0131] In method step 316, finally, a comparison is made between the
pattern 130 ascertained in method step 314 for the pre-defined effector
120, whose effect is to be ascertained, and the at least one pattern 130
of the effectors 120 combined in the at least one class of effectors 220.

[0132] With the aid of FIG. 3B, the method described in FIG. 3A in
abstract terms will be presented with a specific exemplary embodiment. In
the representation shown here, which, for example, again shows a screen
representation of a computer-aided implementation of the method described
in FIG. 3A, one or more effects of the effector 120 of the diethylhexyl
phthalate type are to be determined.

[0133] To this end, a multiplicity of effects 310 are entered in the first
column of the table shown in FIG. 3B. These effects 310 in the
representation are provided by way of example with more or less
characterizing designations. Thus, for example, the designation
"liver_oxidative_stress_m_ld_hd_group_ef--putative--06122007"
may specify a particular type of oxidative stress on the liver. The other
designations in the first column of the table in FIG. 3B show other types
of effects 310, which will not be dealt with in detail here.

[0134] Preferably, a class of effectors or optionally a plurality of
classes of effectors have beforehand been determined for each of these
effects 310, for example with the aid of the method described in FIGS. 2A
to 2D. For example, an class of effectors 220 may be stored for each
effect 310 with an associated pattern 130 of this class of effectors 220.

[0135] Furthermore, the second and third columns of the table according to
FIG. 3B represent in a highly simplified way how a pattern 130 of the
effector 120, whose effect is to be ascertained (in this case by way of
example diethylhexyl phthalate), is compared with the patterns of the
classes of effectors 220. In the exemplary embodiment represented or else
in other exemplary embodiments of the present invention, this is done by
means of, for example, what is known as a Pearson correlation. The latter
is a dimensionless measure of the degree of a linear relationship between
interval-scaled features. The value shown here is the Pearson correlation
coefficient r. In the third column, for each effect 310 or each class of
effectors 220, Pearson correlation coefficients 318 are entered in box
form, including their confidence intervals. These Pearson correlation
coefficients 318 basically indicate the reliability of the patterns of
the classes of effectors 220 which have been determined for example by
means of the iterative method described in FIG. 2B. For a fully reliable
pattern 130, the Pearson correlation coefficient 318 would lie precisely
at +1, in other words in each case on the far right-hand end in the third
column in FIG. 3B, and the uncertainty interval would equal 0.
Alternatively or in addition to the Pearson correlation, or the Pearson
correlation coefficient, other types of correlations or correlation
coefficients may also be employed. For example, Spearman correlations or
Spearman correlation coefficients may be employed as an alternative or in
addition.

[0136] Furthermore, the correlation of the pattern determined in step 314
for the pre-defined effector 120 whose effect is to be determined is
entered in FIG. 3B in the second column numerically and in the third
column in the form of dots, respectively with an uncertainty interval for
each effect 310 or class of effectors 220. What is shown here is in each
case the Pearson correlation coefficient r in numerical form (in the
second column) and as a plot on a scale from -1 (left-hand end) to +1
(right-hand end) in the third column. Thus, this Pearson correlation
coefficient 320 indicates the degree of the match of the pattern 130 of
the effector 120 whose effect is to be determined with the pattern 130 of
the class of effectors 220 in each case. In an ideal case, in other words
when the effector 120 in question has the same effect as the classes of
effectors 220, this Pearson correlation coefficient 320 in the third
column in FIG. 3B should lie at the right-hand end of the scale, in other
words at +1.

[0137] In the specific embodiment in FIG. 3B, the latter is the case in
particular for the first four classes of effectors 220. Accordingly, it
can be stated with high likelihood that the effector in question,
diethylhexyl phthalate, has the same effect as these first four classes
of effectors. A relatively high degree of match can also still be found
in respect of the fifth to eighth classes of effectors 220 in the table
in FIG. 3B.

[0138] On the other hand, there is a comparatively low match for the other
classes of effectors 220. Accordingly, it can be ruled out with a high
likelihood that the effector diethylhexyl phthalate has the same effect
as these classes of effectors 220.

[0139] As a result of the method according to FIGS. 3A and 3B, the effects
of the first four classes of effectors 220 in the table according to FIG.
3B can therefore be assigned with high likelihood to the effector
diethylhexyl phthalate. In this way, a number of effects have here been
ascertained for this effector 120 in the present exemplary embodiment.

[0140] FIGS. 4A and 4B represent an alternative method to FIGS. 3A and 3B
for identifying at least one effect of a pre-defined effector. Again,
FIG. 4A shows a schematic flow chart of this method.

[0141] Method step 410 in FIG. 4A represents a method step in which at
least one pattern 130 of the pre-defined effector 120 whose effect is to
be identified is established. Again, this may be done for example by
means of the method described in FIGS. 1A and 1B.

[0142] Reference number 412 denotes a method step in which a database 116
is provided in which profiles 124 are stored for a multiplicity of
further effectors 120. In respect of this, again, reference can be made
to the exemplary embodiments above.

[0143] Reference number 414 denotes a method step in which a search is
made in the database 116 for effectors 120 with patterns similar or
identical to the pattern 130 established in step 410. This may for
example again be done by means of a comparison using a correlation
method. In this respect, reference may for example again be made to the
description of FIG. 3B, and a similar correlation method may for example
also be employed in method step 414 for comparing the patterns 130.

[0144] In method step 416 a check is carried out as to whether the
effectors 120 ascertained in step 414 (assuming that at least one such
effector 120 has been ascertained--which need not necessarily be the
case) have at least one known effect. This may for example be done by the
effectors 120 ascertained in step 414 already having been assigned to a
class of effectors 220 and/or by again using expert knowledge about the
effectors 120 which have been ascertained.

[0145] Method step 418 represents a conditional method step. Specifically,
if a known effect has been found in method step 416, then the effect of
the pre-defined effector is equated with the known effect (likewise, a
plurality of known effects may be identified). At least one effect of the
effector 120 in question is thereby identified. Otherwise, that is to say
when no known effect is found in step 416, the method in FIG. 4 was
without a result.

[0146] FIG. 4B illustrates this method by way of example with reference to
the example of the effector diethylhexyl phthalate. A pattern 130 is
established for this effector 120, and this pattern 130 is compared with
known patterns 130 of a plurality of other effectors 120 in a database
116. FIG. 4B shows a pictorial representation of a result of this pattern
comparison; since the effector in question, diethylhexyl phthalate, is
also contained in the database 116, it is, in turn, also listed per se in
the representation according to FIG. 4B.

[0147] Furthermore, comparison results of the pattern 130 of the
pre-defined effector diethylhexyl phthalate with the respective pattern
130 of the respective effector 120 are represented in the third column of
the table shown in FIG. 4B for each effector 120. Again, these
comparisons are carried out by way of example by means of a Pearson
correlation. Again, what is shown in each case here is the Pearson
correlation coefficient r. Matches can be found with the aid of these
correlation results, and a ranking may be carried out as a prioritization
according to the degree of the match. The greatest degree of match (rank
1), with a Pearson correlation coefficient r equal to 1, is, of course,
represented by diethylhexyl phthalate itself since the pattern 130 of
this effector 120 naturally gives a perfect match with its own pattern
130.

[0148] As the next-closest pattern, with a Pearson correlation coefficient
r=0.713, an effector 120 denoted here as "treatment 294" was ascertained
in the table according to FIG. 4B. It is therefore to be expected that
the effector in question, diethylhexyl phthalate, has the same or at
least a similar effect to the effector "treatment 294".

[0149] Method steps 416 and 418 are not represented in FIG. 4B. If it is
found for example that the effector "treatment 294" has a known effect
310, for example on the basis of expert knowledge or on the basis of a
known assignment of this effector to a class of effectors 220 with at
least one known effect 310, then, based on the high Pearson correlation
coefficient r equal 0.713, which may for example lie above a pre-defined
match threshold, it can be ascertained that the effector in question,
diethylhexyl phthalate, too, has this effect. At least one effect of this
effector diethylhexyl phthalate has thus been identified.

[0150] In general, for example in the method in FIGS. 4A and 4B, one or
more match thresholds may be pre-defined. These match thresholds may for
example be selected more or less arbitrarily, and may for example be set
above r equals 0.5, preferably r>0.6 and especially preferably
r>0.7. Iterative adaptation of this match threshold is also possible,
for example when additional tests have ascertained that this threshold
has been selected too low, that is to say that the effector 120 in
question has incorrectly been assigned an effect 310 which, in fact, it
does not have.

[0151] The efficacy of the method described in FIGS. 4A and 4B will be
illustrated in greater detail with the aid of FIGS. 5A and 5B. Two
chemically similar substances are studied in this exemplary embodiment,
which are:

##STR00001##

[0152] 2-Acetylaminofluorene is known to be an effector 120 which has the
following effects 310: [0153] potent liver enzyme inducer, [0154] liver
carcinogen, [0155] immunosuppressant, [0156] bladder carcinogen.

[0157] For the chemically similar substance 4-acetylaminofluorene, on the
other hand, it is known that this effector 120 has the following effects
310: [0158] weak liver enzyme inducer, [0159] no liver carcinogen,
[0160] lipid accumulation in the liver, [0161] immunosuppressant.

[0162] Despite the chemical similarity, very different effects 310 of
these effectors 120 can therefore be observed in practice. The question
is whether these different effects can be identified by means of a method
according to the present invention.

[0163] Accordingly, in a representation similar to FIG. 2C, FIG. 5A
represents a comparison of the metabolic profiles 124 of these effectors
2-acetylaminofluorene and 4-acetylaminofluorene with various other
effectors 120. By means of the method described hereinabove, for example
similarly as in FIG. 2C, it is possible in this manner to ascertain
patterns 130 for each of these effectors 120. In a similar representation
to FIG. 4B, on the other hand, FIG. 5B shows a pattern comparison of the
patterns 130 ascertained with the aid of FIG. 5A. A ranking with the aid
of the Pearson correlation coefficients is again represented here, in a
similar representation to FIG. 4B. The left-hand table in FIG. 5B shows a
comparison of the pattern 130 for the effector 2-acetylaminofluorene with
the other effectors 120 in FIG. 5A, and the right-hand table shows a
comparison of the pattern 130 of the effector 4-acetylaminofluorene with
the patterns of the remaining effectors 120 in FIG. 5A.

[0164] Correspondingly, the effector 2-acetylaminofluorene itself in turn
naturally occupies the first position of the ranking in the left-hand
table in FIG. 5B, with a Pearson correlation coefficient r=1. In the
right-hand table, the effector 4-acetylaminofluorene correspondingly
occupies the first position, likewise with a Pearson correlation
coefficient r=1. These effectors in question, 2-acetylaminofluorene and
4-acetylaminofluorene, are, respectively, followed by similar effectors
in order of their similarity.

[0165] In the table excerpt in FIG. 5B at the bottom it can be seen that
the chemically similar effector 4-acetylaminofluorene does not occur in
the left-hand table, in which 2-acetylaminofluorene is compared with the
other effectors 120 in FIG. 5A, until the 282nd position, with a
very low Pearson correlation coefficient of r=0.229.

[0166] These results impressively show that the method described with the
aid of FIGS. 4A and 4B for comparing effects of different effectors 120
reflects the actual situation very well. Furthermore, this exemplary
embodiment shows that even chemically similar effectors 120 may have very
different effects 310.

LIST OF REFERENCE SYMBOLS

[0167] 110 Provision of at least one profile of the pre-defined effector

[0168] 112 Comparison of at least one value of at least one biomarker of
the profile with at least one significance threshold

[0169] 114 Combination of significant biomarkers of the profile to form a
pattern

[0170] 116 Database

[0171] 118 Metabolites

[0172] 120 Effectors

[0173] 122 Biomarkers

[0174] 124 Profile

[0175] 126 Profile for compound 1

[0176] 128 Profile for compound 2

[0177] 130 Pattern

[0178] 210 Specification of at least one effector

[0179] 212 Establishing or updating of at least one pattern of the at
least one effector

[0180] 214 Provision of a database with profiles for further effectors

[0181] 216 Search for effectors with the same similar profiles

[0182] 218 Assignment of the ascertained effectors to the class of
effectors

[0183] 220 Class of effectors

[0184] 310 Effect

[0185] 312 Establishing at least one class of effectors for at least one
known effect

[0186] 314 Establishing at least one pattern of the pre-defined effector

[0187] 316 Comparison of the pattern from step 314 with pattern from step
312

[0188] 318 Pearson correlation coefficient for patterns of the classes of
effectors

[0189] 320 Pearson correlation coefficient for pattern of the effector

[0190] 410 Establishing at least one pattern of the pre-defined effector

[0191] 412 Provision of a database in which profiles are stored for a
multiplicity of further effectors

[0192] 414 Searching in the database for effectors with a pattern similar
or identical to the pattern established in step 410

[0193] 416 Checking whether the effectors ascertained in step 414 have at
least one known effect

[0194] 418 If a known effect is found in step 416, equating the effect of
the pre-defined effector with the known effect